AutoML Leaderboard
AutoML Performance

AutoML Performance Boxplot

Features Importance

Spearman Correlation of Models

Summary of 5_Default_NeuralNetwork
<< Go back
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
2.9 seconds
Metric details
|
score |
threshold |
| logloss |
0.713423 |
nan |
| auc |
0.583903 |
nan |
| f1 |
0.682464 |
0.374215 |
| accuracy |
0.568627 |
0.52678 |
| precision |
0.7 |
0.778995 |
| recall |
1 |
0.148961 |
| mcc |
0.180831 |
0.374215 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.713423 |
nan |
| auc |
0.583903 |
nan |
| f1 |
0.607143 |
0.52678 |
| accuracy |
0.568627 |
0.52678 |
| precision |
0.56044 |
0.52678 |
| recall |
0.662338 |
0.52678 |
| mcc |
0.13853 |
0.52678 |
Confusion matrix (at threshold=0.52678)
|
Predicted as C |
Predicted as N |
| Labeled as C |
36 |
40 |
| Labeled as N |
26 |
51 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of 1_Baseline
<< Go back
Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
0.4 seconds
Metric details
|
score |
threshold |
| logloss |
0.693164 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.669565 |
0.449015 |
| accuracy |
0.503268 |
0.449015 |
| precision |
0.503268 |
0.449015 |
| recall |
1 |
0.449015 |
| mcc |
0 |
0.449015 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.693164 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.669565 |
0.449015 |
| accuracy |
0.503268 |
0.449015 |
| precision |
0.503268 |
0.449015 |
| recall |
1 |
0.449015 |
| mcc |
0 |
0.449015 |
Confusion matrix (at threshold=0.449015)
|
Predicted as C |
Predicted as N |
| Labeled as C |
0 |
76 |
| Labeled as N |
0 |
77 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of Ensemble
<< Go back
Ensemble structure
| Model |
Weight |
| 4_Default_Xgboost |
1 |
Metric details
|
score |
threshold |
| logloss |
0.679375 |
nan |
| auc |
0.622437 |
nan |
| f1 |
0.673469 |
0.474164 |
| accuracy |
0.614379 |
0.503049 |
| precision |
0.857143 |
0.568181 |
| recall |
1 |
0.371206 |
| mcc |
0.230151 |
0.50418 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.679375 |
nan |
| auc |
0.622437 |
nan |
| f1 |
0.609272 |
0.503049 |
| accuracy |
0.614379 |
0.503049 |
| precision |
0.621622 |
0.503049 |
| recall |
0.597403 |
0.503049 |
| mcc |
0.229099 |
0.503049 |
Confusion matrix (at threshold=0.503049)
|
Predicted as C |
Predicted as N |
| Labeled as C |
48 |
28 |
| Labeled as N |
31 |
46 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of 2_DecisionTree
<< Go back
Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
11.0 seconds
Metric details
|
score |
threshold |
| logloss |
0.889368 |
nan |
| auc |
0.519481 |
nan |
| f1 |
0.663717 |
0 |
| accuracy |
0.542484 |
0.385421 |
| precision |
0.571429 |
0.627753 |
| recall |
0.974026 |
0 |
| mcc |
0.114445 |
0.385421 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.889368 |
nan |
| auc |
0.519481 |
nan |
| f1 |
0.663462 |
0.385421 |
| accuracy |
0.542484 |
0.385421 |
| precision |
0.526718 |
0.385421 |
| recall |
0.896104 |
0.385421 |
| mcc |
0.114445 |
0.385421 |
Confusion matrix (at threshold=0.385421)
|
Predicted as C |
Predicted as N |
| Labeled as C |
14 |
62 |
| Labeled as N |
8 |
69 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back
Summary of 6_Default_RandomForest
<< Go back
Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
13.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.7221 |
nan |
| auc |
0.535031 |
nan |
| f1 |
0.669565 |
0.113565 |
| accuracy |
0.581699 |
0.502156 |
| precision |
0.574713 |
0.502156 |
| recall |
1 |
0.113565 |
| mcc |
0.164058 |
0.502156 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.7221 |
nan |
| auc |
0.535031 |
nan |
| f1 |
0.609756 |
0.502156 |
| accuracy |
0.581699 |
0.502156 |
| precision |
0.574713 |
0.502156 |
| recall |
0.649351 |
0.502156 |
| mcc |
0.164058 |
0.502156 |
Confusion matrix (at threshold=0.502156)
|
Predicted as C |
Predicted as N |
| Labeled as C |
39 |
37 |
| Labeled as N |
27 |
50 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back
Summary of 4_Default_Xgboost
<< Go back
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
57.0 seconds
Metric details
|
score |
threshold |
| logloss |
0.679375 |
nan |
| auc |
0.622437 |
nan |
| f1 |
0.673469 |
0.474164 |
| accuracy |
0.614379 |
0.503049 |
| precision |
0.857143 |
0.568181 |
| recall |
1 |
0.371206 |
| mcc |
0.230151 |
0.50418 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.679375 |
nan |
| auc |
0.622437 |
nan |
| f1 |
0.609272 |
0.503049 |
| accuracy |
0.614379 |
0.503049 |
| precision |
0.621622 |
0.503049 |
| recall |
0.597403 |
0.503049 |
| mcc |
0.229099 |
0.503049 |
Confusion matrix (at threshold=0.503049)
|
Predicted as C |
Predicted as N |
| Labeled as C |
48 |
28 |
| Labeled as N |
31 |
46 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back
Summary of 3_Linear
<< Go back
Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
5.8 seconds
Metric details
|
score |
threshold |
| logloss |
0.834594 |
nan |
| auc |
0.559809 |
nan |
| f1 |
0.669565 |
0.022978 |
| accuracy |
0.581699 |
0.467724 |
| precision |
0.666667 |
0.88009 |
| recall |
1 |
0.022978 |
| mcc |
0.163438 |
0.467724 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.834594 |
nan |
| auc |
0.559809 |
nan |
| f1 |
0.6 |
0.467724 |
| accuracy |
0.581699 |
0.467724 |
| precision |
0.578313 |
0.467724 |
| recall |
0.623377 |
0.467724 |
| mcc |
0.163438 |
0.467724 |
Confusion matrix (at threshold=0.467724)
|
Predicted as C |
Predicted as N |
| Labeled as C |
41 |
35 |
| Labeled as N |
29 |
48 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back